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Yanchao Wang

Publications and source records attributed to Yanchao Wang.

At least 37 records · Page 2Linked to original sources

Creating cyclo-N$_5$$^{+}$ cation and assembling N$_5$$^{+}$N$_5$$^{-}$ salt via electronegativity co-matching in tailored ionic compounds

The recent discovery of crystalline pentazolates marks a major advance in polynitrogen science and raises prospects of making the long-touted potent propellant N$_5$$^{+}$N$_5$$^{-}$ salt. However, despite the synthesis of cyclo-N$_5$$^{-}$ anion in pentazolates, counter cation cyclo-N$_5$$^{+}$ remains elusive due to the strong oxidizing power of pentazole ion; moreover, pure N$_5$$^{+}$N$_5$$^{-}$ salt is known to be unstable. Here, we devise a new strategy for making rare cyclo-N$_5$$^{+}$ cation and assembling the long-sought N$_5$$^{+}$N$_5$$^{-}$ salt in tailored ionic compounds, wherein the negative/positive host ions act as oxidizing/reducing agents to form cyclo-N$_5$$^{+}$/N$_5$$^{-}$ species. This strategy is implemented via an advanced computational crystal structure search, which identifies XN$_5$N$_5$F (X = Li, Na, K) compounds that stabilize at high pressures and remain viable at ambient pressure-temperature conditions based on \textit{ab initio} molecular dynamics simulations. This finding opens an avenue for creating and stabilizing N$_5$$^{+}$N$_5$$^{-}$ salt assembly in ionic compounds, where cyclo-N$_5$ species are oxidized/reduced via co-matching with host ions of high/low electronegativity. The present results demonstrate novel polynitrogen chemistry, and these findings offer new insights and prospects in the design and synthesis of diverse chemical species that exhibit unusual charge states, bonding structures, and superior functionality.

cond-mat.mtrl-sci↗

Engineering ferroelectricity in monoclinic hafnia

Ferroelectricity in the complementary metal-oxide semiconductor (CMOS)-compatible hafnia (HfO$_2$) is crucial for the fabrication of high-integration nonvolatile memory devices. However, the capture of ferroelectricity in HfO$_2$ requires the stabilization of thermodynamically-metastable orthorhombic or rhombohedral phases, which entails the introduction of defects (e.g., dopants and vacancies) and pays the price of crystal imperfections, causing unpleasant wake-up and fatigue effects. Here, we report a theoretical strategy on the realization of robust ferroelectricity in HfO$_2$-based ferroelectrics by designing a series of epitaxial (HfO$_2$)$_1$/(CeO$_2$)$_1$ superlattices. The advantages of the designated ferroelectric superlattices are defects free, and most importantly, on the base of the thermodynamically stable monoclinic phase of HfO$_2$. Consequently, this allows the creation of superior ferroelectric properties with an electric polarization $>$25 $μ$C/cm$^2$ and an ultralow polarization-switching energy barrier at $\sim$2.5 meV/atom. Our work may open an entirely new route towards the fabrication of high-performance HfO$_2$ based ferroelectric devices.

cond-mat.mtrl-sci↗

Highly spin-polarized carriers and strong ferromagnetism in doped perovskite antiferromagnetic semiconductors

In semiconductor spintronics, the generation of highly spin-polarized carriers and the efficient probe of spin order (due to strong ferromagnetism) -- at or above room temperature -- are crucial because it allows for the design of spin-based semiconductor devices. Usually, such goals were fulfilled in room-temperature ferromagnetic semiconductors, being rare materials in nature. While room-temperature antiferromagnetic semiconductors are plentiful, the possibility for creating highly spin-polarized carriers and strong ferromagnetism in these materials remain to be unraveled. Here, we explore such a possibility by first-principles simulations, working with CaTcO$_3$ and NaOsO$_3$ perovskites -- being room-temperature antiferromagnetic semiconductors. We find that doping them by electrons or holes results in these materials to be highly spin-polarized, carrying enormous ferromagnetic moments. Doping electrons with moderate carrier density can yield strong ferromagnetism in them, with the ferromagnetic moments being comparable to that in typical ferromagnetic semiconductors. Our work thus indicates the merit of perovskite antiferromagnetic semiconductors in spintronics -- for a possible replacement of ferromagnetic semiconductors.

cond-mat.mtrl-sci↗

Data-Driven Prediction of Complex Crystal Structures of Dense Lithium

Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for new yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal phases containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the newly observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine-learning potentials.

cond-mat.mtrl-sci↗

Zeeman effect in centrosymmetric antiferromagnets controlled by an electric field

Centrosymmetric antiferromagnetic semiconductors, although abundant in nature, seem less promising than ferromagnets and ferroelectrics for practical applications in semiconductor spintronics. As a matter of fact, the lack of spontaneous polarization and magnetization hinders the efficient utilization of electronic spin in these materials. Here, we propose a paradigm to harness electronic spin in centrosymmetric antiferromagnets via Zeeman spin splittings of electronic energy levels -- termed as spin Zeeman effect -- which is controlled by electric field.By symmetry analysis, we identify twenty-one centrosymmetric antiferromagnetic point groups that accommodate such a spin Zeeman effect. We further predict by first-principles that two antiferromagnetic semiconductors, Fe$_2$TeO$_6$ and SrFe$_2$S$_2$O, are excellent candidates showcasing Zeeman splittings as large as $\sim$55 and $\sim$30 meV, respectively, induced by an electric field of 6 MV/cm. Moreover, the electronic spin magnetization associated to the splitting energy levels can be switched by reversing the electric field. Our work thus sheds light on the electric-field control of electronic spin in antiferromagnets, which broadens the scope of application of centrosymmetric antiferromagnetic semiconductors.

cond-mat.mtrl-sci↗

Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. Numerical results validate that our method has higher classification accuracy compared to previous methods.

eess.SP↗

Synthesis and Superconductivity in yttrium superhydrides under high pressure

The flourishing rare earth superhydrides are a class of recently discovered materials that possess near-room-temperature superconductivity at high pressures, opening a new era of superconductivity research at high pressures. Among these superhydrides, yttrium superhydrides attracted great interest owing to their abundance of stoichiometries and excellent superconductivities. Here, we carried out a comprehensive study of yttrium superhydrides in a wide pressure range of 145-300 GPa. We successfully synthesized a series of superhydrides with the compositions of YH4, YH6, YH7, and YH9, and reported their superconducting transition temperatures of 82 K at 167 GPa, 218 K at 165 GPa, 29 K at 162 GPa, and 230 K at 300 GPa, respectively, which were evidenced by a sharp drop of resistivity. The structure and superconductivity of YH4, which was taken as a representative example, were also examined by X-ray diffraction measurements and the suppression of the superconductivity under external magnetic fields, respectively. Clathrate YH10 as a candidate of room-temperature superconductor was not synthesized within the studied pressure and temperature ranges of up to 300 GPa and 2000 K, respectively. The current work created a detailed platform for further searching room-temperature superconductors in polynary yttrium-based superhydrides.

cond-mat.supr-con↗

Nonlocal Pseudopotential Energy Density Functional for Orbital-Free Density Functional Theory

Orbital-free density functional theory (OF-DFT) runs at low computational cost that scales linearly with the number of simulated atoms, making it suitable for large-scale material simulations. It is generally considered that OF-DFT strictly requires the use of local pseudopotentials, rather than orbital-dependent nonlocal pseudopotentials, for the calculation of electron-ion interaction energies, as no orbitals are available. This is unfortunate situation since the nonlocal pseudopotentials are known to give much better transferability and calculation accuracy than local ones. We report here the development of a theoretical scheme that allows the direct use of nonlocal pseudopotentials in OF-DFT. In this scheme, a nonlocal pseudopotential energy density functional is derived by the projection of nonlocal pseudopotential onto the non-interacting density matrix (instead of 'orbitals') that can be approximated explicitly as a functional of electron density. Our development defies the belief that nonlocal pseudopotentials are not applicable to OF-DFT, leading to the creation of an alternate theoretical framework of OF-DFT that works superior to the traditional one.

cond-mat.mtrl-sci↗

A new theoretical model of optical pumped solid-state laser

In this paper, from a new perspective of the time (pump time, relaxation time, and stimulated emission time) of the cycle for the active particles at each energy level, the concept of the number of iterative pumping of the laser medium is introduced, and the equivalent model of the laser when it emits light in a steady state is established. By deriving the number of times that the laser medium is repeatedly pumped in the light exit area when the light is emitted, the analytical expression of the laser output power is deduced, and the law of the laser output power changing with the laser parameters is obtained. By fitting and comparing with the experimental results, the new model and the experimental results are in good agreement, the related parameters obtained are also very consistent with the literature, and the changes of multiple parameters and temperature are obtained. These results, especially the expression for the analysis of the laser output power pave a new way for the research and design of optical pumped lasers.

physics.optics↗

A Symmetry-orientated Divide-and-Conquer Method for Crystal Structure Prediction

Crystal structure prediction has been a subject of topical interest, but remains a substantial challenge, especially for complex structures as it deals with the global minimization of the extremely rugged high-dimensional potential energy surface. In this manuscript, a symmetry-orientated divide-and-conquer scheme was proposed to construct a symmetry tree graph, where the entire search space is decomposed into a finite number of symmetry-dependent subspaces. An artificial intelligence-based symmetry selection strategy was subsequently devised to select the low-lying subspaces with high symmetries for global exploration and in-depth exploitation. Our approach can significantly simplify the problem of crystal structure prediction by avoiding exploration of the most complex P1 subspace on the entire search space and have the advantage of preserving the crystal symmetry during structure evolution, making it well suitable for predicting the complex crystal structures. The effectiveness of the method has been validated by successful prediction of the candidate structures of binary Lennard-Jones mixtures and high-pressure phase of ice, containing more than one hundred atoms in the simulation cell. The work, therefore, opens up an opportunity towards achieving the long-sought goal for crystal structure prediction of complex systems.

cond-mat.mtrl-sci↗

Stabilization of S3O4 at High Pressure-Implications for the Sulfur Excess Paradox

The geological conundrum of sulfur excess refers to the finding that predicted amounts of sulfur, in the form of SO2, discharged in volcanic eruptions much exceeds the sulfur available for degassing from the erupted magma. Exploring the source of the excess sulfur has been the subject of considerable interest. Here, from a systematic computational investigation of sulfur-oxygen compounds under pressure, a hitherto unknown S3O4 compound containing a mixture of sulfur oxidation states +II and +IV emerges and is predicted to be stabilized above a pressure of 79 GPa. We predict that S3O4 can be produced via multiple redox reactions involving subducted S-bearing minerals (e.g., sulfates and sulfides) at high pressure conditions relevant to the deep lower mantle, and conversely be decomposed into SO2 and S at shallow depths of Earth. Therefore, S3O4 can be considered as a key intermediate compound to promote the decomposition of sulfates to release SO2, which offers an alternative source of the excess sulfur released during explosive eruptions. These findings provide a possible resolution to the geological paradox of excess sulfur degassing and a viable mechanism for the understanding of S exchange between surface and the lower mantle for the deep sulfur cycle.

physics.comp-ph↗

Electronic nature of chiral charge order in the kagome superconductor CsV3Sb5

Kagome superconductors with Tc up to 7K have been discovered over 40 years. Recently, unconventional chiral charge order has been reported in kagome superconductor KV3Sb5, with an ordering temperature of one order of magnitude higher than the TC. However, the chirality of the charge order has not been reported in the cousin kagome superconductor CsV3Sb5, and the electronic nature of the chirality remains elusive. In this letter, we report the observation of electronic chiral charge order in CsV3Sb5 via scanning tunneling microscopy (STM). We observe a 2x2 charge modulation and a 1x4 superlattice in both topographic data and tunneling spectroscopy. 2x2 charge modulation is highly anticipated as a charge order by fundamental kagome lattice models at van Hove filling, and is shown to exhibit intrinsic chirality. We find that the 1x4 superlattices forms various small domain walls, and can be a surface effect as supported by our first-principles calculations. Crucially, we find that the amplitude of the energy gap opened by the charge order exhibits real space modulations, and features 2x2 wave vectors with chirality, highlighting the electronic nature of the chiral charge order. STM study at 0.4K reveals a superconducting energy gap with a gap size 2Δ=0.85meV, which estimates a moderate superconductivity coupling strength with 2Δ/kBTc=3.9. When further applying a c-axis magnetic field, vortex core bound states are observed within this gap, indicative of clean-limit superconductivity.

cond-mat.str-el↗

A short-range metastable defect in the double layer ice

Although the phase of water has extensively investigated whether there exists a defect distorting only locally the structure still under debate. Here we report a localized 5775 defect phase presented in the double layer ice on the Au (111) surface, which is a metastable structure with 5- and 7-membered rings compared with a perfect hexagonal one. Without altering the total number of the hydrogen bonds of the ice, the defect only introduces 0.08 Å molecular displacement and 3.27% interaction energy change outside the defected area. Such defect also exists without Au support but causes a larger lattice relaxation or smaller interaction energy change. The excessively high barrier as well as the low quantum tunneling and thermodynamic probabilities hinder the formation of the defect by post-grown isomerization from the perfect to the defected structure. This finding indicates that the defected ice is stable, and the defect can be formed during the ice growth stage.

cond-mat.mtrl-sci↗

Learning-Based Real-Time Event Identification Using Rich Real PMU Data

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU time series and imperfect data quality could bring great technical challenges to real-time system event identification. To address these issues, this paper proposes a two-stage learning-based framework. At the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify operation events. The proposed method fully builds on and is also tested on a large real dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.

eess.SP↗

Nonlocal Kinetic Energy Density Functionals for Isolated Systems via Local Density Approximation Kernels

Despite a large number of nonlocal kinetic energy density functionals (KEDFs) available for large-scale calculations, most of those nonlocal KEDFs designed for the extended systems cannot be directly applied to isolated systems. In this manuscript, we proposed a generalized scheme to construct nonlocal KEDFs via the local density approximation kernels and construct a family of KEDFs for simulations of isolated systems within orbital-free density functional theory. The performance of KEDFs has been demonstrated by several clusters encompassing Mg, Si and GaAs. The results show that our constructed KEDFs can achieve high numerical accuracy and stability for random clusters, therefore, making orbital-free density functional theory accessible for practical simulations of isolated systems.

cond-mat.mtrl-sci↗

Metallic liquid H3O in a thin-shell zone inside Uranus and Neptune

The Solar System harbors deep unresolved mysteries despite centuries-long study. A highly intriguing case concerns anomalous non-dipolar and non-axisymmetric magnetic fields of Uranus and Neptune that have long eluded explanation by the prevailing theory. A thin-shell dynamo conjecture captures observed phenomena but leaves unexplained fundamental material basis and underlying mechanism. Here, we report the discovery of trihydrogen oxide (H3O) in metallic liquid state stabilized at extreme pressure and temperature conditions inside these icy planets. Calculated stability pressure field compared to known pressure-radius relation for Uranus and Neptune places metallic liquid H3O in a thin-shell zone near planetary cores. These findings from accurate quantum mechanical calculations rationalize the empirically conjectured thin-shell dynamo model and establish key physical benchmarks that are essential to elucidating the enigmatic magnetic-field anomaly of Uranus and Neptune, resolving a major mystery in planetary science.

physics.comp-ph↗

Accelerating CALYPSO Structure Prediction by Data-driven Learning of Potential Energy Surface

Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and material discovery. However, they are generally restricted to small systems owing to the heavy computational cost of underlying density functional theory (DFT) calculations. In this work, by combining state-of-art machine learning (ML) potential with our in-house developed CALYPSO structure prediction method, we developed two acceleration schemes for structure prediction toward large systems, in which ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches. The developed schemes have been applied to medium- and large-sized boron clusters, which are challenging cases for both construction of ML potentials and extensive structure searches. Experimental structures of B36 and B40 clusters can be readily reproduced, and the putative global minimum structure for B84 cluster is proposed, where substantially less computational cost by several orders of magnitude is evident if compared with full DFT-based structure searches. Our results demonstrate a viable route for structure prediction toward large systems via the combination of state-of-art structure prediction methods and ML techniques.

cond-mat.mtrl-sci↗

Construction of crystal structure prototype database: methods and applications

Crystal structure prototype data have become a useful source of information for materials discovery in the fields of crystallography, chemistry, physics, and materials science. This work reports the development of a robust and efficient method for assessing the similarity of structures on the basis of their interatomic distances. Using this method, we proposed a simple and unambiguous definition of crystal structure prototype based on hierarchical clustering theory, and constructed the Crystal Structure Prototype Database (CSPD) by filtering the known crystallographic structures in a database. With similar method, a program Structure Prototype Analysis Package (SPAP) was developed to remove similar structures in CALYPSO prediction results and extract predicted low energy structures for a separate theoretical structure database. A series of statistics describing the distribution of crystal structure prototypes in the CSPD was compiled to provide an important insight for structure prediction and high-throughput calculations. Illustrative examples of the application of the proposed database are given, including the generation of initial structures for structure prediction and determination of the prototype structure in databases. These examples demonstrate the CSPD to be a generally applicable and useful tool for materials discovery.

cond-mat.mtrl-sci↗